Thromboembolism after coronavirus disease 2019 vaccination in atrial fibrillation/flutter: a self-controlled case series study.

BACKGROUND AND AIMS: Concerns about the safety of coronavirus disease 2019 (COVID-19) vaccines in patients with atrial fibrillation/flutter (AF/AFL) have arisen due to reports of thrombo-embolic events following COVID-19 vaccination in the general population. This study aimed to evaluate the risk of thrombo-embolic events after COVID-19 vaccination in patients with AF/AFL. METHODS: This was a modified self-controlled case-series study using a comprehensive nationwide-linked database provided by the National Health Insurance Service in South Korea to calculate incidence rate ratios (IRRs) of thrombo-embolic events. The study population included individuals aged ≥12 years who were either vaccinated (e.g. one or two doses) or unvaccinated during the period from February to December 2021. The primary outcome was a composite of thrombo-embolic events, including ischaemic stroke, transient ischaemic attack, and systemic thromboembolism. The risk period was defined as 0-21 days following COVID-19 vaccination. RESULTS: The final analysis included 124 127 individuals with AF/AFL. The IRR of thrombo-embolic events within 21 days after COVID-19 vaccination, compared with that during the unexposed control period, was 0.93 [95% confidence interval (CI) 0.77-1.12]. No significant risk variations were noted by sex, age, or vaccine type. However, patients without anticoagulant therapy had an IRR of 1.88 (95% CI 1.39-2.54) following vaccination. CONCLUSIONS: In patients with AF/AFL, COVID-19 vaccination was generally not associated with an increased risk of thrombo-embolic events. However, careful individual risk assessment is required when advising vaccination for those not on oral anticoagulant, as these patients exhibited an increased risk of thrombo-embolic events post-vaccination.

Comparative cardiovascular outcomes in type 2 diabetes patients taking dapagliflozin versus empagliflozin: a nationwide population-based cohort study.

BACKGROUND: Sodium-glucose co-transporter-2 inhibitors displayed cardiovascular benefits in type 2 diabetes mellitus in previous studies; however, there were some heterogeneities regarding respective cardiovascular outcomes within the class. Furthermore, their efficacies in Asians, females, and those with low cardiovascular risks were under-represented. Thus, we compared the cardiovascular outcomes between new users of dapagliflozin and empagliflozin in a broad range of patients with type 2 diabetes mellitus using a nationwide population-based real-world cohort from Korea. METHODS: Korean National Health Insurance registry data between May 2016 and December 2018 were extracted, and an active-comparator new-user design was applied. The primary outcome was a composite of heart failure (HF)-related events (i.e., hospitalization for HF and HF-related death), myocardial infarction, ischemic stroke, and cardiovascular death. The secondary outcomes were individual components of the primary outcome. RESULTS: A total of 366,031 new users of dapagliflozin or empagliflozin were identified. After 1:1 nearest-neighbor propensity score matching, 72,752 individuals (mean age approximately 56 years, 42% women) from each group were included in the final analysis, with a follow-up of 150,000 ~ person-years. Approximately 40% of the patients included in the study had type 2 diabetes mellitus as their sole cardiovascular risk factor, with no other risk factors. The risk of the primary outcome was not different significantly between dapagliflozin and empagliflozin users (hazard ratio [HR] 0.93, 95% confidence interval [CI] 0.855-1.006). The risks of secondary outcomes were also similar, with the exception of the risks of HF-related events (HR 0.84, 95% CI 0.714-0.989) and cardiovascular death (HR 0.76, 95% CI 0.618-0.921), which were significantly lower in the dapagliflozin users. CONCLUSIONS: This large-scale nationwide population-based real-world cohort study revealed no significant difference in composite cardiovascular outcomes between new users of dapagliflozin and empagliflozin. However, dapagliflozin might be associated with lower risks of hospitalization or death due to HF and cardiovascular death than empagliflozin in Asian patients with type 2 diabetes mellitus.

A Method of Generating Real-Time Natural Light Color Temperature Cycle for Circadian Lighting Service.

The light intensity and color temperature of natural light periodically change and promote the circadian entrainment of the human body. In addition, the color temperature cycle of natural light that is unique to each region is formed by its location and geographic and environmental factors, affecting the health of its residents. Research on lighting and construction to provide the color temperature of real-time natural light has continued to provide the beneficial effect of natural indoor lighting. However, lighting technology that provides the real-time color temperature of natural light could not be realized since it is challenging to select a color temperature cycle zone due to abrupt color temperature changes at sunrise and sunset. Such drastic shifts cause an irregular measurement of color temperature over time due to general weather or atmospheric conditions. In a previous study, a method of generating a color temperature cycle using deep learning was introduced, but the performance at the beginning and end of the color temperature cycle was unreliable. Therefore, this study proposes generating a real-time natural light color temperature cycle for the circadian lighting service. The characteristics of the daily color temperature cycle were analyzed based on the measured natural light characteristics database, and a data set for learning was established. To improve the color temperature cycle generation performance, a deep learning (TadGAN) model was implemented by searching for the lowest point of the color temperature at the start and end points of the color temperature cycle and applying the boot and ending datasets to these points. The color temperature cycle zone was accurately detected in real-time in the experiment, and the generation performance of the color temperature cycle was maintained at the beginning and end of the color temperature cycle. The mean absolute error decreased by about 67%, confirming the generation of a more accurate real-time color temperature cycle.

TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light.

This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking sensors. Analysis results for the measured natural light showed that irregular color temperature cycles were observed for more than 90% of the year due to the influence of regional weather and atmospheric conditions. Regular color temperature cycles were observed only on some clear days. The color temperature cycle dramatically affects the health of the occupants. However, since irregular color temperatures are difficult to predict and cannot easily generate cycles, only the color temperatures of some clear days are currently used, and the actual color temperature of natural light cannot be reproduced. There is little research on deriving real-time periodic characteristics and lighting services targeting irregular color temperatures of natural light. Therefore, this paper proposes a TadGAN (Time Series Anomaly Detection Using Generative Adversarial Networks)-based daily color temperature cycle generation method that responds to irregular changes in the natural light color temperature. A TadGAN model for generating the natural light color temperature cycle was built, and learning was performed based on the dataset extracted through the measured natural light characteristic Database. After that, the generator of TadGAN was repeatedly applied to generate a color temperature cycle close to the change of natural light. In the performance test of the proposed method, it was possible to generate periodic characteristics of the irregular natural light color temperature distribution.

Association between exercise habits and stroke, heart failure, and mortality in Korean patients with incident atrial fibrillation: A nationwide population-based cohort study.

BACKGROUND: There is a paucity of information about cardiovascular outcomes related to exercise habit change after a new diagnosis of atrial fibrillation (AF). We investigated the association between exercise habits after a new AF diagnosis and ischemic stroke, heart failure (HF), and all-cause death. METHODS AND FINDINGS: This is a nationwide population-based cohort study using data from the Korea National Health Insurance Service. A retrospective analysis was performed for 66,692 patients with newly diagnosed AF between 2010 and 2016 who underwent 2 serial health examinations within 2 years before and after their AF diagnosis. Individuals were divided into 4 categories according to performance of regular exercise, which was investigated by a self-reported questionnaire in each health examination, before and after their AF diagnosis: persistent non-exercisers (30.5%), new exercisers (17.8%), exercise dropouts (17.4%), and exercise maintainers (34.2%). The primary outcomes were incidence of ischemic stroke, HF, and all-cause death. Differences in baseline characteristics among groups were balanced considering demographics, comorbidities, medications, lifestyle behaviors, and income status. The risks of the outcomes were computed by weighted Cox proportional hazards models with inverse probability of treatment weighting (IPTW) during a mean follow-up of 3.4 ± 2.0 years. The new exerciser and exercise maintainer groups were associated with a lower risk of HF compared to the persistent non-exerciser group: the hazard ratios (HRs) (95% CIs) were 0.95 (0.90-0.99) and 0.92 (0.88-0.96), respectively (p < 0.001). Also, performing exercise any time before or after AF diagnosis was associated with a lower risk of mortality compared to persistent non-exercising: the HR (95% CI) was 0.82 (0.73-0.91) for new exercisers, 0.83 (0.74-0.93) for exercise dropouts, and 0.61 (0.55-0.67) for exercise maintainers (p < 0.001). For ischemic stroke, the estimates of HRs were 10%-14% lower in patients of the exercise groups, yet differences were statistically insignificant (p = 0.057). Energy expenditure of 1,000-1,499 MET-min/wk (regular moderate exercise 170-240 min/wk) was consistently associated with a lower risk of each outcome based on a subgroup analysis of the new exerciser group. Study limitations include recall bias introduced due to the nature of the self-reported questionnaire and restricted external generalizability to other ethnic groups. CONCLUSIONS: Initiating or continuing regular exercise after AF diagnosis was associated with lower risks of HF and mortality. The promotion of exercise might reduce the future risk of adverse outcomes in patients with AF.

Diverse ß-lactam antibiotic-resistant bacteria and microbial community in milk from mastitic cows.

Intramammary bacterial infection, the most common cause of mastitis, is the most costly disease in dairy cattle in the US and reason for antibiotic usage. Ceftiofur, a third-generation cephalosporin, is generally used to treat such disease, but it has a high treatment failure rate. Though the reason is not known clearly, it is hypothesized that multiple factors are associated with the treatment failure. In this study, we analyzed 169 milk samples from cows with mastitis in two independent dairy farms (Farm A and B) in which 19.4% (Farm A) and 14.3% (Farm B) of the antibiotic treated cows were not cured. The prevalence of cephalosporin-resistant bacteria (CRB) in milk was 72.0% and 42.1% in Farm A and B, respectively. Nineteen and nine bacterial genera were identified in Farm A and B respectively, with the most abundant genus being Staphylococcus (27.1%; Farm A) and Bacillus (63.5%; Farm B). However, no strong relationship between the treatment failure rate and the CRB prevalence was observed. Furthermore, the metagenomic analysis showed no significant differences in the α- and ß-diversities of microbiota in milk samples from cured and uncured cows, suggesting that antibiotic-resistant bacteria were not the sole reason for the antibiotic treatment failure. KEY POINTS: • The mastitic milk samples had high prevalence of cephalosporin-resistant bacteria (CRB). • The CRB identified belong to diversified species. • Antibiotic treatment failure was not solely caused by the abundance of CRB.

A DNN-Based UVI Calculation Method Using Representative Color Information of Sun Object Images.

As outdoor activities are necessary for maintaining our health, research interest in environmental conditions such as the weather, atmosphere, and ultraviolet (UV) radiation is increasing. In particular, UV radiation, which can benefit or harm the human body depending on the degree of exposure, is recognized as an essential environmental factor that needs to be identified. However, unlike the weather and atmospheric conditions, which can be identified to some extent by the naked eye, UV radiation corresponds to wavelength bands that humans cannot recognize; hence, the intensity of UV radiation cannot be measured. Recently, although devices and sensors that can measure UV radiation have been launched, it is very difficult for ordinary users to acquire ambient UV radiation information directly because of the cost and inconvenience caused by operating separate devices. Herein, a deep neural network (DNN)-based ultraviolet index (UVI) calculation method is proposed using representative color information of sun object images. First, Mask-region-based convolutional neural networks (R-CNN) are applied to sky images to extract sun object regions and then detect the representative color of the sun object regions. Then, a deep learning model is constructed to calculate the UVI by inputting RGB color values, which are representative colors detected later along with the altitude angle and azimuth of the sun at that time. After selecting each day of spring and autumn, the performance of the proposed method was tested, and it was confirmed that accurate UVI could be calculated within a range of mean absolute error of 0.3.

IoT-Based Research Equipment Sharing System for Remotely Controlled Two-Photon Laser Scanning Microscopy.

In this study, two-photon laser scanning microscopy (TPLSM) based on the internet of things (IoT) is proposed as a remote research equipment sharing system, which enables the remote sharing economy. IoT modules, where data are transmitted to and received from the remote users in the web service via IoT, instead of a data acquisition (DAQ) system embedded in the conventional TPLSM, are installed in the IoT-based TPLSM (IoT-TPLSM). The performance for each IoT module is evaluated independently, and it is confirmed that it works well even in a personal computer-free environment. In addition, a message queuing telemetry transport (MQTT) protocol is applied to the DAQ interface in the web service, and a graphic user interface for enabling the remote users to operate IoT-TPLSM remotely is also designed and implemented. For the image acquisition demonstration, the stained cellular images and the autofluorescent tissue images are obtained in IoT-TPLSM. Lastly, it is confirmed that the comparable performance is provided with the conventional TPLSM by evaluating the imaging conditions and qualities of the three-dimensional image stacks processed in IoT-TPLSM.

Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User's Location.

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays' information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user's location obtained with mobile devices' help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model's data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user's location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90-95% in the summers, as well as in winters.

Method of Calculating Short-Wavelength-Ratio-Based Color Temperature Supporting the Measurement of Real-Time Natural Light Characteristics through RGB Sensor.

The characteristics of natural light are mostly collected through specialized measuring equipment, such as a spectroradiometer, and some suggested measurement methods through a small RGB sensor. However, specialized measuring equipment presents difficulty in its high cost, and the RGB-sensor-based method has the limitation of being unable to measure the wavelength characteristics of natural light that are needed to implement lighting that supports circadian rhythms. This paper presents a method for calculating the short-wavelength-ratio-based color temperature of natural light in real time. First, an analysis of the correlation between the characteristics of natural light collected through a spectroradiometer was performed to determine the factors that were needed to accurately measure the color temperature of natural light. Then, the short-wavelength ratio of natural light was calculated through chromaticity coordinates (x and y), which are output values of the RGB sensor, and an equation for calculating the color temperature of natural light was derived through the short-wavelength ratio. Furthermore, after producing an RGB-sensor-based device, the derived equation was applied to calculate the color temperature of real-time natural light that reflects the wavelength characteristics. Then, as a result of the performance evaluation of the proposed method, the color temperature of natural light was accurately calculated within 1% of the average error rate.

Development of a UV Index Sensor-Based Portable Measurement Device with the EUVB Ratio of Natural Light.

Ultraviolet (UV) rays are electromagnetic waves that account for about 5% of solar light, and when overexposed, they pose malevolent effects on human skin and health. However, with recent reports on the beneficial effects of some wavelength bands of UV rays, people's interest in UV information has increased. This has resulted in requiring not just simple information, such as the amount of UV or UV index (UVI), but detailed UV information that directly affects health, such as EUVB (erythemally weighted UVB). However, calculating EUVB, which can be done by applying the erythemal weighted function on the intensity value in wavelength, requires specialized optical measurement devices, which cannot be easily accessed by the general public; furthermore, public institutions' UV information services do not offer EUVB information for individuals. Therefore, the present study proposes a UVI sensor-based portable measurement device, with which the general public can have easy access to UV-related information. The proposed device comprises a UVI sensor that can measure the intensity of erythemal UV radiation, a Bluetooth Low Energy (BLE) module that supports communication, and a micro controller unit (MCU) for key operations. In addition, it applies the ratio of EUVB by month/time, resulting from the actual analysis of natural light to calculate the EUVB and provides the amount of UVI and EUVB to check if they meet conditions required for outdoor activities through the device and smartphone applications. The applicability of the proposed device was verified by the measurement performance comparison test with the standard device, a spectrometer (CAS 140 CT), which showed an average error of 0.045 for UVI and 0.0014 W/m². The proposed device's offering of UV-related information such as UVI and EUVB to the user is expected to prevent potential damage due to exposure to UV and to support healthy outdoor activities.

Comparative effects of oxygenates-gasoline blended fuels on the exhaust emissions in gasoline-powered vehicles.

This study aimed to investigate the comparative effects of oxygenates such as ethanol (EA), methyl tertiary-butyl ether (MTBE), and ethyl tertiary butyl ether (ETBE) by fixing the oxygen contents as 0.82 wt% 1.65 wt%, and 2.74 wt% of the fuels on the regulated (CO, NMHC and NOx) and unregulated (formaldehyde, acetaldehyde and BTEX) exhaust emissions in gasoline-powered vehicles. The most widely used type of vehicles (light-duty, medium-duty, heavy-duty) in Korea were tested on a chassis dynamometer under the CVS-75 Cycle. When EA, MTBE and ETBE percentage increased, the CO and NMHC concentration decreased. The NOx emission decreased at 1.65 wt% and 2.74 wt% oxygen content of MTBE and ETBE. The emissions of CO decreased by 0.363 g/km, 0.266 g/km and 0.356 g/km for light-duty vehicle when EA, MTBE and ETBE oxygenates blending ratio increased. Increased EA, MTBE and ETBE oxygenates blending ratio demonstrated no specific reducing effect on CO emissions from low-mileage vehicle, but NMHC emissions decreased by 0.011 g/km (medium-duty), 0.015 g/km (light-duty) and 0.018 g/km (heavy-duty). More CO was emitted from MTBE among three oxygenates at same oxygen content. The emitted concentrations of NMHC from three oxygenates at same oxygen content were almost similar, but reduced NOx emissions from EA (10%) to MTBE (20.4%) and ETBE (23.6%) were observed at 2.74 wt% oxygen content. Reducing effect on CO emissions was order of EA > ETBE > MTBE. Formaldehyde emissions increased up to 54.3% as MTBE ratio increased. When oxygen content of ETBE, EA, and MTBE increased from 0.82 wt% to 2.74 wt%, the acetaldehyde emissions increased up to 177.4%, 39.5% and 31.0%, respectively. There was significant formaldehyde concentration difference between high emission vehicle type (light-duty and medium-duty) and low emission vehicle type (heavy-duty and low-mileage) for three oxygenates. Reduction effect of MTBE and ETBE on BTEX was the order of toluene > benzene > ethylbenzene > xylene, and MTBE showed more reduction effect than ETBE at same oxygen content.

Mechanisms of lysophosphatidylcholine-induced demyelination: A primary lipid disrupting myelinopathy.

For decades lysophosphatidylcholine (LPC, lysolecithin) has been used to induce demyelination, without a clear understanding of its mechanisms. LPC is an endogenous lysophospholipid so it may cause demyelination in certain diseases. We investigated whether known receptor systems, inflammation or nonspecific lipid disruption mediates LPC-demyelination in mice. We found that LPC nonspecifically disrupted myelin lipids. LPC integrated into cellular membranes and rapidly induced cell membrane permeability; in mice, LPC injury was phenocopied by other lipid disrupting agents. Interestingly, following its injection into white matter, LPC was cleared within 24 hr but by five days there was an elevation of endogenous LPC that was not associated with damage. This elevation of LPC in the absence of injury raises the possibility that the brain has mechanisms to buffer LPC. In support, LPC injury in culture was significantly ameliorated by albumin buffering. These results shed light on the mechanisms of LPC injury and homeostasis.

Characterization of odorous gases at landfill site and in surrounding areas.

Concentration levels and seasonal variation of odorous gases at landfill site and in surrounding areas within the city of Incheon, South Korea were investigated. Sampling was conducted at 11 points (5 at landfill site and 6 in surrounding areas). The highest concentrations of odorous gases (complex odor, ammonia, acetaldehyde, and VOCs) at landfill site were found in summer, probably due to fast decomposition of waste in high temperature related with more release of ammonia. In addition, specific weather condition of dominant wind direction, humidity and higher atmospheric pressure with no or lower wind speed caused positive effect of higher aldehyde compounds and VOCs concentration. Similar to other studies, sludge-related sampling site S-2, where a couple of odor generating facilities including sludge mixing and drying treatment process are located, showed the highest concentration levels of odorous gases compared to other sites. Odor generation frequency was in the order of acetaldehyde (68.8%) > ammonia (39.4%) > propionaldehyde (21.9%), which means the main substances generating the unpleasant odor at landfill site was recognized as aldehydes and ammonia due to combined effect of sludge-related facilities and meteorological conditions. Offensive odor was not a big pollution issue in most surrounding areas which are located within a circle of 5 km radius of the landfill except high odor generation frequency of acetaldehyde and propionaldehyde. Relative percentage differences (RPD) of odorous gases between day and night times at landfill site were below 10%, which indicates that the concentration differences in day and night were not severe. The relationship between concentrations of complex odor and designated offensive odor substances was analyzed statistically. At landfill site, the analysis shows that the correlation coefficient between the concentration of complex odor and ammonia was quite high (0.833), but it was much lower (0.129) in the surrounding areas due to considerably lower concentrations of these substances.

Characterization of particulate matter from diesel passenger cars tested on chassis dynamometers.

Emission characterization of particle number as well as particle mass from three diesel passenger cars equipped with diesel particulate filter (DPF), diesel oxidation catalyst (DOC) and exhaust gas recirculation (EGR) under the vehicle driving cycles and regulatory cycle. Total particle number emissions (PNEs) decreased gradually during speed-up of vehicle from 17.3 to 97.3km/hr. As the average vehicle speed increases, the size-segregated peak of particle number concentration shifts to smaller size ranges of particles. The correlation analysis with various particulate components such as particle number concentration (PNC), ultrafine particle number concentration (UFPNC) and particulate matter (PM) mass was conducted to compare gaseous compounds (CO, CO2, HC and NOx). The UFPNC and PM were not only emitted highly in Seoul during severe traffic jam conditions, but also have good correlation with hydrocarbons and NOx influencing high potential on secondary aerosol generation. The effect of the dilution temperature on total PNC under the New European Driving Cycle (NEDC), was slightly higher than the dilution ratio. In addition, the nuclei mode (DP: ≤13nm) was confirmed to be more sensitive to the dilution temperature rather than other particle size ranges. Comparison with particle composition between vehicle speed cycles and regulatory cycle showed that sulfate was slightly increased at regulatory cycle, while other components were relatively similar. During cold start test, semivolatile nucleation particles were increased due to effect of cold environment. Research on particle formation dependent on dilution conditions of diesel passenger cars under the NEDC is important to verify impact on vehicular traffic and secondary aerosol formation in Seoul.

Acute fatal pericardial effusion induced by accidental ingestion of cigarette butts in a dog.

A dog was referred for collapse and tachypnea after ingesting cigarette butts. Thoracic radiography and echocardiography indicated pericardial effusion, and an electrocardiogram showed tachycardia, variable QRS complexes, and ventricular premature complexes. This is the first description of fatal pericardial effusion associated with cigarette butt ingestion in a veterinary patient.

Effusion péricardique aiguë mortelle induite par l'ingestion accidentelle de mégots de cigarette chez un chien. Un chien a été recommandé pour un collapsus et une tachypnée après l'ingestion de mégots de cigarette. Une radiographie thoracique et une échocardiographie ont indiqué une effusion péricardique et un électrocardiogramme a montré la tachycardie, des complexes QRS variables et des complexes ventriculaires prématurés. Il s'agit de la première description d'une effusion péricardique mortelle associée à l'ingestion de mégots de cigarettes chez un patient vétérinaire.(Traduit par Isabelle Vallières).

Characterizations of organic compounds in diesel exhaust particulates.

To characterize how the speed and load of a medium-duty diesel engine affected the organic compounds in diesel particle matter (PM) below 1 µm, four driving conditions were examined. At all four driving conditions, concentration of identifiable organic compounds in PM ultrafine (34-94 nm) and accumulation (94-1000 nm) modes ranged from 2.9 to 5.7 µg/m(3) and 9.5 to 16.4 µg/m(3), respectively. As a function of driving conditions, the non-oxygen-containing organics exhibited a reversed concentration trend to the oxygen-containing organics. The identified organic compounds were classified into eleven classes: alkanes, alkenes, alkynes, aromatic hydrocarbons, carboxylic acids, esters, ketones, alcohols, ethers, nitrogen-containing compounds, and sulfur-containing compounds. At all driving conditions, alkane class consistently showed the highest concentration (8.3 to 18.0 µg/m(3)) followed by carboxylic acid, esters, ketones and alcohols. Twelve polycyclic aromatic hydrocarbons (PAHs) were identified with a total concentration ranging from 37.9 to 174.8 ng/m(3). In addition, nine nitrogen-containing polycyclic aromatic compounds (NPACs) were identified with a total concentration ranging from 7.0 to 10.3 ng/m(3). The most abundant PAH (phenanthrene) and NPACs (7,8-benzoquinoline and 3-nitrophenanthrene) comprise a similar molecular (3 aromatic-ring) structure under the highest engine speed and engine load.

LED context lighting system in residential areas.

As issues of environment and energy draw keen interest around the globe due to such problems as global warming and the energy crisis, LED with high optical efficiency is brought to the fore as the next generation lighting. In addition, as the national income level gets higher and life expectancy is extended, interest in the enhancement of life quality is increasing. Accordingly, the trend of lightings is changing from mere adjustment of light intensity to system lighting in order to enhance the quality of one's life as well as reduce energy consumption. Thus, this study aims to design LED context lighting system that automatically recognizes the location and acts of a user in residential areas and creates an appropriate lighting environment. The proposed system designed in this study includes three types of processing: first, the creation of a lighting environment index suitable for the user's surroundings and lighting control scenarios and second, it measures and analyzes the optical characteristics that change depending on the dimming control of lighting and applies them to the index. Lastly, it adopts PIR, piezoelectric, and power sensor to grasp the location and acts of the user and create a lighting environment suitable for the current context.

Minimization of temperature ranges between the top and bottom of an air flow controlling device through hybrid control in a plant factory.

To maintain the production timing, productivity, and product quality of plant factories, it is necessary to keep the growth environment uniform. A vertical multistage type of plant factory involves different levels of growing trays, which results in the problem of difference in temperature among vertically different locations. To address it, it is necessary to install air flow devices such as air flow fan and cooling/heating device at the proper locations in order to facilitate air circulation in the facility as well as develop a controlling technology for efficient operation. Accordingly, this study compares the temperature and air distribution within the space of a vertical multistage closed-type plant factory by controlling cooling/heating devices and air flow fans harmoniously by means of the specially designed testbed. The experiment results indicate that in the hybrid control of cooling and heating devices and air flow fans, the difference in temperature decreased by as much as 78.9% compared to that when only cooling and heating devices were operated; the air distribution was improved by as much as 63.4%.

Cooling Performance Prediction of Particle-Based Radiative Cooling Film Considering Particle Size Distribution.

Here, we present a novel protocol concept for quantifying the cooling performance of particle-based radiative cooling (PBRC). PBRC, known for its high flexibility and scalability, emerges as a promising method for practical applications. The cooling power, one of the cooling performance indexes, is the typical quantitative performance index, representing its cooling capability at the surface. One of the primary obstacles to predicting cooling power is the difficulty of simulating the non-uniform size and shape of micro-nanoparticles in the PBRC film. The present work aims to develop an accurate protocol for predicting the cooling power of PBRC film using image processing and regression analysis techniques. Specifically, the protocol considers the particle size distribution through circle object detection on SEM images and determines the probability density function based on a chi-square test. To validate the proposed protocol, a PBRC structure with PDMS/Al2O3 micro-nanoparticles is fabricated, and the proposed protocol precisely predicts the measured cooling power with a 7.8% error. Through this validation, the proposed protocol proves its potential and reliability for the design of PBRC.